METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.
RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.
PURPOSE: This study aims to develop an instrument with good psychometric properties, based on the Rasch measurement model and confirmatory factor analysis.
METHODS: The study consists of two phases of pilot and field studies involving 597 year four students from IBWS MOE.
RESULTS: The findings from the Rasch measurement model analysis have shown that 54 items meet the criteria of the item fit, unidimensionality, and reliability index. Meanwhile, confirmatory factor analysis found that 44 items have shown a valid item fit index.
CONCLUSIONS: The combination of both analyses has shown the strength of 10IBLP-I psychometric properties that cover the aspects of validity and reliability. The findings also provide an implication to the theory, with empirical evidence that the IB learner profile consists of 10 constructs. Besides, the evidenced 10IBLP-I comprises good psychometric properties, which can be used to measure the level of IB learner profile among IBWS MOE students to assess the effectiveness of the implementation of IBMYP in Malaysia.
PURPOSE: To identify the influence of teachers' self-efficacy and school administrators' transformational leadership practices on teachers' innovative behaviour.
METHOD: A quantitative approach using a cross-sectional survey design with a sample of 1415 teachers from four states in Malaysia, and the data were statistically analysed using SPSS® version 26.0 for Windows™ (IBM Corporation, New York, NY, USA).
RESULT: Multiple Regression Analysis found that teachers' self-efficacy and school administrators' transformational leadership practices both had a significant influence on teachers' innovative behaviour by contributing 47.0% of the variance in teachers' innovative behaviour.
CONCLUSION: The findings suggested that teachers' self-efficacy and school administrators' transformational leadership practices both play a role in influencing teachers' innovative behaviour. Therefore, the stakeholders need to consider the aspects of self-efficacy and transformational leadership practices of school administrators in drafting policies and related programmes to improve teachers' innovative behaviour.